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haldensify
View on CRAN: Click
here
Download and install haldensify package within the R console
Install from CRAN:
install.packages("haldensify")
Install from Github:
library("remotes")
install_github("cran/haldensify") Install by package version:
library("remotes")
install_version("haldensify", "0.2.8") Attach the package and use:
library("haldensify")
Maintained by
Nima Hejazi
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-03-14
Latest Update: 2025-09-01
Description:
An algorithm for flexible conditional density estimation based on
application of pooled hazard regression to an artificial repeated measures
dataset constructed by discretizing the support of the outcome variable. To
facilitate non/semi-parametric estimation of the conditional density, the
highly adaptive lasso, a nonparametric regression function shown to reliably
estimate a large class of functions at a fast convergence rate, is utilized.
The pooled hazards data augmentation formulation implemented was first
described by D
How to cite:
Nima Hejazi (2020). haldensify: Highly Adaptive Lasso Conditional Density Estimation. R package version 0.2.8, https://cran.r-project.org/web/packages/haldensify. Accessed 18 Sep. 2026.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for haldensify
Functions, R codes and Examples using
the haldensify R package
Some associated functions: confint.ipw_haldensify . cv_haldensify . dcar_selector . fit_haldensify . format_long_hazards . gcv_selector . haldensify . ipw_shift . make_bins . map_hazard_to_density . plateau_selector . plot.haldensify . predict.haldensify . print.haldensify . print.ipw_haldensify .
Some associated R codes: confint.R . haldensify.R . ipw_shift.R . plots.R . predict.R . selector_dcar.R . selector_gcv.R . selector_plateau.R . utils.R . zzz.R . Full haldensify package functions and examples
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